AURAL reaches CoT-RL-comparable performance on speech language models with 11.8x faster time to first answer token via latent reasoning and joint chunk prediction
The work introduces AURAL, which models a distribution over multiple plausible reasoning continuations in latent space and jointly predicts chunks of future states, and with AuralReason-683K (683K bilingual speech utterances, about 1,000 hours) for initial supervision plus AURAL-RL, it achieves performance comparable to CoT-RL across two backbones with larger gains over the respective supervised checkpoints on most metrics, and on Qwen2.5-Omni reduces time to the first answer token by 11.8x, from 1.22 to 0.10 s, versus 0.05 s for direct answering.